<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>A Multi-Source Reconstruction of the 2024 Jasper Wildfire Complex Evacuation and Reentry Periods</title>
      <link>https://trid.trb.org/View/2679408</link>
      <description><![CDATA[Wildfire events pose extreme danger to residents, requiring evacuations to safety. To better inform evacuation planning, a post-disaster reconstruction of events using multiple sources can identify emergent travel behavior, evacuation time estimates (ETEs), departure timings, and flow characteristics. In this study, the 2024 Jasper Wildfire Complex was reconstructed with de-identified network mobility data from the TELUS Data for Good Program, open-source traffic counts from the Alberta Government, and official government correspondence via social media platforms. By leveraging unique insights from three different data sources, the authors develop a more complete understanding of the evacuation across the entire duration of the wildfire compared to single-source studies. The results found value in operating a unified command between municipalities and counties, a peak hours-long evacuation flow estimate of 800 vehicles per hour on a one-lane rural highway, and a steady rate of 2,000 device departures per hour that best matched Poisson, logistic, and Rayleigh functions. The evacuation of the Jasper area was estimated to take eight hours, limited by the single-lane egress route, indicating that a combination of transportation strategies is required for timing reductions in similar jurisdictions. A slow, weeks-long rate of return was observed during reentry, indicating the importance of long-term community supports and resources. Positively, strong similarities between network mobility data and traffic counts showcase their data validity in investigating future evacuation events. The results are most applicable to rural communities with a single egress route and a similar hazard context as Jasper.]]></description>
      <pubDate>Thu, 09 Apr 2026 10:07:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679408</guid>
    </item>
    <item>
      <title>Drilling for disaster preparedness: Insights from a community wildfire evacuation exercise</title>
      <link>https://trid.trb.org/View/2670421</link>
      <description><![CDATA[Community evacuation exercises (or drills) are one approach residents and authorities use to train wildfire emergency procedures in the Wildland Urban Interface (WUI). This paper presents results from a drill performed in Roxborough Park, a WUI community in Colorado, USA, in 2024. Observer and self-report data were collected to derive resident preparatory actions, pre-travel, travel, and total evacuation times, as well as route choice and the drill’s impact on their perceived preparedness. It took more than 28 minutes until 90 % of residents began traveling and more than 48 minutes until 90 % had completed their evacuation. Most of the participants reported following the instructions for the evacuation route, with a minority taking a shorter or more familiar route. The work underlines the value of drills for improving community disaster preparedness, providing data for developing/testing computational models, and deepening our understanding of human behavior in wildfire scenarios.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2670421</guid>
    </item>
    <item>
      <title>Optimizing evacuation routes for human mobility during wildfires: A case study of the 2023 McDougall Creek Wildfire</title>
      <link>https://trid.trb.org/View/2656107</link>
      <description><![CDATA[Wildfires present significant challenges to evacuation planning due to their dynamic nature, rapid spread, and the limitations of static routing methods, which often lead to congestion and increased safety risks. This study addresses these issues by analyzing the 2023 McDougall Creek Wildfire (Kelowna, BC) using GPS data, identifying congestion bottlenecks, and developing a dynamic Dijkstra-A* algorithm with a multi-criteria cost function that integrates distance, congestion, and fire risk. Validated through SUMO simulations across two scenario groups with fire origins in the northwest and southwest, we tested four evacuation strategies: simultaneous departure, temporally staggered departure, region-based evacuation with uniform response times, and region-based evacuation with realistic response time variability. Our results demonstrate that region-based, staggered evacuations with realistic response times effectively reduce peak congestion and improve safety compared to simultaneous approaches. This research highlights the potential of GPS-informed behavior and hazard-aware routing to improve adaptive evacuation strategies for climate-driven wildfire events.]]></description>
      <pubDate>Mon, 26 Jan 2026 08:41:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2656107</guid>
    </item>
    <item>
      <title>Assessment of intended electric vehicle charging behaviours during wildfire evacuations</title>
      <link>https://trid.trb.org/View/2640979</link>
      <description><![CDATA[Electric vehicle (EV) adoption is a growing challenge for disaster planning, requiring resilient grids and strategies. With minimal research on EV user behaviour in an evacuation context, this study addresses this gap by developing a series of discrete choice models to understand the factors that impact EV charging behaviour in a hypothetical wildfire evacuation. Through a non-probability panel from the Canadian provinces of Alberta and British Columbia of people living in high/medium fire risk, we collected survey data (n = 1371) on intended actions, assuming a 400 km range EV. Results indicate diverse EV charging patterns, and no single charging location type nor one form of charging behaviour dominated across scenarios throughout the evacuation time period. Across all models, we found that EV ownership, a preference to reduce risk to property and family, intended evacuation choices, and past hazard experience influenced charging behaviour. Targeted grid improvements and strategic placement of both fixed and mobile charging stations would likely be sufficient to meet electricity demand from EVs in evacuations.]]></description>
      <pubDate>Mon, 22 Dec 2025 10:59:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2640979</guid>
    </item>
    <item>
      <title>Using Social Media to Model Community-Based Behavioral Response During Public Health Emergencies: A Case Study of the 2023 Canadian Wildfires</title>
      <link>https://trid.trb.org/View/2640202</link>
      <description><![CDATA[New York City (NYC) experienced severe air pollution from Canadian wildfires in June 2023, disrupting travel and daily activities. This study analyzed public reactions to evacuation, indoor activities, shopping, and recreation using geotagged X posts during the air pollution crisis. Geotagged posts were reverse geocoded to census blocks and spatially joined with socioeconomic and demographic data from the U.S. census and American Community Survey. The dataset initially comprised 0.59 million geotagged X posts from 66,858 unique users in NYC over a 1-week period. After relevance filtering, the final dataset included 10,258 posts from 10,258 unique users on wildfire-related travel and activity discussions. Public reactions were analyzed using a BERT-based natural language processing model, whereas a gender–race model inferred users’ gender and racial identities based on their first and last names. A multinomial logit model assessed how socioeconomic and demographic factors influenced activity discussions during the crisis. The findings revealed demographic differences in responses. For instance, females were less likely to discuss evacuation and essential trips, possibly owing to continued workplace operations despite hazardous conditions. Racial differences were also evident, with Asians more frequently mentioning evacuation and commuting, whereas African Americans showed lower engagement in discussions about social and recreational activities. Socioeconomic disparities further influenced response patterns, as lower-income and less-educated groups expressed fewer concerns about evacuation, highlighting potential barriers to crisis awareness and preparedness. These insights emphasize the need for targeted communication strategies and equitable health interventions to ensure that emergency responses effectively reach vulnerable populations during environmental crises.]]></description>
      <pubDate>Mon, 15 Dec 2025 09:25:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2640202</guid>
    </item>
    <item>
      <title>Driving behaviour during flood and bushfire emergency evacuations: Insights from observational and self-reported data</title>
      <link>https://trid.trb.org/View/2625985</link>
      <description><![CDATA[Climate-related emergencies such as floods and bushfires are among the most prevalent natural hazards globally. During these events, individuals often drive to self-evacuate; however, doing so through floodwaters or bushfire-affected areas poses significant risks to both evacuees and emergency responders. Understanding the factors that influence driver decision-making in these situations is crucial, as it relates directly to pre-evacuation delays, compliance with evacuation orders, and the safety of volunteer rescue personnel. It also informs more effective risk communication and policy design. This study adopts a mixed-methods approach by integrating content analysis of self-recorded real-life driving videos with surveys and discrete choice experiments. It examines both strategic and operational dimensions of driver behavior during flood and bushfire conditions. The video analysis captures driver actions, environmental cues, and emotional or verbal responses, while the choice experiment investigates how risk perception, environmental severity, social cues, and contextual factors shape the decision to proceed through hazardous routes. Findings suggest most participants prefer to avoid driving through flood or bushfire scenarios in hypothetical contexts. Environmental severity—such as floodwater depth or fire intensity—was the strongest deterrent. However, the perceived presence of other drivers emerged as a strong motivating factor. Observational data also show that driving mostly occurred when other vehicles were present. Younger and male participants reported greater willingness to drive in both hazards—a pattern also mirrored in the video observations. This dual-method approach offers new insights into emergency driving behavior and holds practical value for shaping public messaging, emergency planning, and policy interventions during natural hazards.]]></description>
      <pubDate>Fri, 05 Dec 2025 14:07:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2625985</guid>
    </item>
    <item>
      <title>Wildfire Evacuation Choice-Making among Underserved Groups in Alberta and British Columbia</title>
      <link>https://trid.trb.org/View/2628306</link>
      <description><![CDATA[Wildfires continue to threaten multiple regions in Canada, and evacuations are often the primary means of ensuring life safety. Understanding how people make decisions before and during wildfire evacuations is thus important in informing preparedness and planning. This research collected survey data between May and July 2023, from residents living in high to moderate fire-risk areas of Alberta and British Columbia (N?=?2,868) to understand their intended evacuation behavior and choice-making during a future wildfire event. Our analysis focuses on underserved groups (people with disabilities, older adults, lower-income households, visible minorities, and carless residents), often neglected in evacuation planning processes. We contribute to the literature by uniquely focusing on decision-making within distinct underserved groups, rather than simply using these identities as variables within a broader model. Estimated logit models offer insight into factors affecting evacuation departure timing, destination and route choices, mode choices, and preferred shelter types. Results suggested that factors such as perceived risk, previous evacuation experiences, and intersecting vulnerabilities have a significant influence on group choices. For example, whereas risk perception significantly influenced evacuation timing among people with disabilities, sociodemographic characteristics were significant in determining shelter choices among older adults. These findings have important implications for enhancing equitable wildfire evacuations, pointing to the need for tailored strategies that consider the needs, barriers, and decision-making patterns of underserved groups. We provide several policy recommendations for local agencies, including ensuring multimodal evacuation plans with transit and shared mobility considerations and providing targeted support for those with intersecting vulnerabilities.]]></description>
      <pubDate>Mon, 24 Nov 2025 15:07:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2628306</guid>
    </item>
    <item>
      <title>Analyzing evacuation route choices during the 2021 Marshall Fire</title>
      <link>https://trid.trb.org/View/2624222</link>
      <description><![CDATA[Knowledge of household evacuation behavior in wildfires is critical to creating effective emergency plans. Understanding evacuation route choices is especially important for both evacuation management strategies and traffic simulations during emergencies. Existing studies of evacuation route choice rely solely on survey or interview data, which have inherent limitations in spatiotemporal resolution and memory bias. This study proposes a new methodology to analyze evacuation route choices and complement existing data sources by leveraging a GPS dataset. The proposed methodology includes a framework for systematically handling sparse GPS data and testing common assumptions used by existing evacuation simulations, i.e. whether evacuees take the shortest path, make a single trip to their destination, or are at home at the start of the evacuation. The authors applied this new method to a sample of 155 evacuees from the 2021 Marshall Fire in Boulder County, CO, which resulted in the evacuation of over 30,000 people. The authors found that the majority of evacuees approximated the shortest path (67.1%), made multiple trips while evacuating to their final destination (64.7%), and were home at the start of the evacuation (60%). The findings of this study can be used to inform better plans for future emergencies and enhance traffic simulations of evacuation behavior.]]></description>
      <pubDate>Mon, 24 Nov 2025 10:19:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2624222</guid>
    </item>
    <item>
      <title>Wildfire Emergency Response and Evacuation Framework Using Drones: Phase I</title>
      <link>https://trid.trb.org/View/2606553</link>
      <description><![CDATA[Wildfires are escalating in frequency and severity due to climate change, posing increasing threats to human life, infrastructure, and ecosystems. Traditional wildfire management systems struggle to respond effectively to rapidly evolving fire conditions. This research presents a novel, artificial intelligence (AI)-powered Wildfire Emergency Response and Evacuation Framework that integrates autonomous unmanned aerial vehicles (UAVs, aka “drones”), multi-sensor data fusion, and machine learning (ML) for real-time fire detection, evacuation planning, and search and rescue (SAR) operations. Central to the system is the Dynamic Wildfire Response Algorithm (DWRA), a hybrid decision-making framework combining AI-driven techniques including reinforcement learning (RL), genetic algorithms (GA), and deep learning that enable adaptive and data-driven response coordination. The system uses infrared (IR) and visible-spectrum cameras, LiDAR, weather sensors, and a data processing technique called edge computing (e.g., NVIDIA Jetson AGX Orin) to provide on-board intelligence and low-latency decision-making without cloud reliance. The combination of these sensors and innovations enables the new framework to respond to wildfires with accuracy and efficiency unseen in traditional management systems. Extensive prototype testing of the framework demonstrated a fivefold improvement in fire detection speed, 28% faster evacuation times, and a 35% increase in SAR efficiency over traditional methods. The results confirm the viability of this framework as a scalable, autonomous solution to wildfire emergencies, with the potential to significantly reduce response time, improve situational awareness, and save lives in high-risk fire zones. Future work will focus on expanding drone swarm capabilities, integrating real-time emergency service communication, and enhancing endurance through solar-powered charging infrastructure. By combining innovative technology with real-time adaptability, this research lays the foundation for a next-generation wildfire response system that is faster, smarter, and better equipped to meet the growing challenges of a world grappling with the effects of climate change.]]></description>
      <pubDate>Mon, 20 Oct 2025 13:40:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2606553</guid>
    </item>
    <item>
      <title>Host community logistics and advanced preparation: Insights from wildfire evacuations in Alberta</title>
      <link>https://trid.trb.org/View/2597074</link>
      <description><![CDATA[This paper examines the experiences of three host communities in Alberta involved in supporting and accommodating evacuees during the record 2023 wildfire season in Canada. Exploring host community service provision is a small but important part of a growing body of research that attempts to understand challenges faced by hosts when evacuations take place. Wildfire displacements can have long-lasting and deep social impacts, and the ability of host communities’ to implement socially aware and holistic emergency management plans and coordinate with key stakeholders is an important area of inquiry. When accommodating evacuees, hosts may face social services strains, uncertainties regarding cost recovery, and traffic and hospitality congestion. As such, this paper deploys case study methods seeking answers to three research questions stemming from the 2023 Alberta Wildfires: 1) How well prepared were communities to host evacuees? 2) What challenges did they encounter? and 3) What lessons can be learned from their experiences? Semi-structured interviews (n = 27) across the three cases of High Level, Whitecourt, and Hinton revealed important insights regarding community preparedness, community-specific challenges, and cross-cutting lessons learned. Major findings demonstrate the importance of intercommunity coordination, rapid needs assessments of incoming evacuees, planning efforts to provide adequate accommodations, and increased awareness around disaster relief programs and incident command training.]]></description>
      <pubDate>Mon, 13 Oct 2025 08:48:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2597074</guid>
    </item>
    <item>
      <title>Resilience assessment in post-wildfire recovery of road transport networks by dynamic thresholds and characteristic curves</title>
      <link>https://trid.trb.org/View/2570519</link>
      <description><![CDATA[Understanding and enhancing the resilience of transport networks against climate-induced extreme events, such as wildfires, is critical to minimizing disruptions and their societal impacts. In this context, resilience is essential for effectively coping with these hazards, as road disruptions can hinder evacuation efforts, reduce accessibility, and lead to significant economic losses. Despite scientific progress, existing resilience assessment frameworks have limitations, including scenario-specific results and limited consideration of the underlying resilience concepts. To address these limitations, this paper introduces a resilience framework based on dynamic thresholds and characteristic curves to evaluate system recovery capacity. The framework incorporates a temporal dimension, allowing for the analysis of recovery time and recovery rate, which depend on the resources available for recovery activities. The characteristic curves illustrate system resilience by encompassing key information about preparedness, response, and recovery capacities inherent to each network. Consequently, the framework offers a more comprehensive view of system behavior during the recovery stage, as demonstrated through its application to a Portuguese case study. The insights gained can assist stakeholders in determining the feasibility of strengthening system resilience through enhanced response and recovery efforts, as well as in identifying when it is critical to reinforce resilience at earlier stages through adaptation measures.]]></description>
      <pubDate>Fri, 26 Sep 2025 13:39:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2570519</guid>
    </item>
    <item>
      <title>Improving wildfire resilience of road networks through generative models</title>
      <link>https://trid.trb.org/View/2577347</link>
      <description><![CDATA[Wildfires pose a significant threat to road networks by causing blockages, structural degradation, and impeding vehicular movement, which complicates emergency response and evacuation efforts. It is crucial to comprehensively evaluate wildfire risks and strategically enhance the improvement measures for road networks, such as capacity expansion for evacuation purposes. This paper introduces a comprehensive optimization framework aimed at enhancing the resilience of road networks in wildfire-prone regions. The proposed framework integrates wildfire simulation, vulnerability assessment, and decision-making strategies for widening critical road segments to improve network resilience. Using a Generative Adversarial Network (GAN)-based model, the framework simulates potential wildfire ignition and propagation scenarios, combining synthetic data with historical weather patterns to assess wildfire risks. Critical network performance metrics—safety, connectivity, reliability, and efficiency—are synthesized into a multi-dimensional network performance tensor (NPT), allowing for systematic analysis and optimal improvement decisions. The framework is implemented on a large road network in the hillside region of Los Angeles, an area exposed to wildfire hazards. The results demonstrate that this framework can effectively prioritize capital improvements for enhancing road network resilience, offering valuable insights and strategic guidance for mitigating wildfire risks. This capital improvement framework has the potential to be adapted and generalized for addressing other natural hazards as well as for other infrastructure networks from a risk-optimal perspective.]]></description>
      <pubDate>Fri, 26 Sep 2025 13:39:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2577347</guid>
    </item>
    <item>
      <title>Best Practices for TxDOT on Handling Wildfires</title>
      <link>https://trid.trb.org/View/2572377</link>
      <description><![CDATA[Texas saw a record-setting number of wildfires in 2010 and 2011. Personnel from the Texas Department of Transportation (TxDOT) are often called upon to provide support in responding to wildfires, and the number of requests has increased dramatically over the past few years. TxDOT developed a draft Guidance Document for Wildfire Response, but personnel are frequently asked to perform services not specifically addressed in that document. TxDOT took advantage of the recent increase in wildfire response experiences to document the lessons learned from wildfire events and study the role of TxDOT in the mitigation, containment, and response to wildfires. The objective of this research project was to develop a protocol to help TxDOT effectively respond to wildfire situations that may occur in the state, and to present the protocol in the form of “Best Practices” based on information gathered from many sources both within TxDOT and from agencies outside the department. Using the information collected, researchers developed a training course for TxDOT personnel who deal with wildfire situations. A pilot course was presented to the Project Monitoring Committee, and based on feedback from that pilot course, six training modules were developed to present as training for TxDOT supervisors, assistants, and district safety coordinators. A significant and repeated finding is that TxDOT employees are not expected to fight fires directly and have no such responsibilities. Emphasis on employee safety is paramount.]]></description>
      <pubDate>Sat, 20 Sep 2025 11:55:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2572377</guid>
    </item>
    <item>
      <title>Proactive Planning Tool to Reduce Wildfire Sedimentation Risks</title>
      <link>https://trid.trb.org/View/2577017</link>
      <description><![CDATA[The geospatial modeling framework includes Direct Geohazard Impact Tools, which estimate debris flow triggering rainfall intensities and sediment erosion along transportation corridors based on predicted wildfire burn severity scenarios, and Downstream Impact Tools, which assess sediment delivery from hillslope erosion and debris flows to river networks. The outputs from these analyses are structured for seamless integration with watershed-scale sediment transport models, such as the Network Sediment Transporter (NST), to further evaluate downstream infrastructure risks. To demonstrate the utility of this approach, it was applied to five high-risk transportation corridors across Utah. These case studies illustrate how proactive assessments can inform infrastructure planning by identifying locations most vulnerable to post-fire sedimentation impacts. The proactive planning framework outlined in this report provides transportation agencies with a scientifically robust and user-friendly method for mitigating future wildfire-induced sediment hazards, enhancing infrastructure resilience, and supporting data-driven decision-making.]]></description>
      <pubDate>Tue, 09 Sep 2025 09:30:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2577017</guid>
    </item>
    <item>
      <title>Geospatial Toolkit for Rapid Assessment of Post-Wildfire Sedimentation Risks to Infrastructure</title>
      <link>https://trid.trb.org/View/2577016</link>
      <description><![CDATA[The Utah Department of Transportation (UDOT) Post-Wildfire Geohazard Assessment Toolkit is a GIS-based toolset designed to evaluate post-wildfire sediment hazards. The toolkit integrates precompiled geospatial datasets with user-supplied burn severity data to estimate debris flow probability, sediment production, and sediment delivery to river networks. The geospatial toolkit includes Direct Geohazard Impact Tools, which predicts debris flow triggering rainfall intensities and sediment volumes, and Downstream Impact Tools, which determine how much sediment is delivered to rivers from hillslope erosion and debris flows. The toolkit outputs spatially attributed datasets that integrated into 1-D watershed scale sediment transport models such as the Network Sediment Transporter (NST). To demonstrate its effectiveness, the toolkit was applied to five wildfire-impacted transportation corridors in Utah, identifying high-risk areas for sediment impacts. Results indicate that the toolkit provides a streamlined and scientifically robust method for assessing post-fire geohazards, supporting transportation agencies in emergency response planning and risk mitigation.]]></description>
      <pubDate>Tue, 09 Sep 2025 09:30:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2577016</guid>
    </item>
  </channel>
</rss>